Evaluating face recognition algorithms in view of image classification features affected by smart makeup

ABSTRACT

Systems and methods for evaluating face recognition algorithms in view of image classification features affected by smart makeup are provided. Increasingly, face recognition technology is being used in applications beyond biometric identification for authentication/login purposes. Face recognition technology has been deployed as part of surveillance cameras, which may capture facial images that can be used as evidence of criminal conduct in a court of law. While admissibility of such evidence is subject to the rules of evidence used for any other piece of evidence, reliance on such face recognition technology poses challenges. Until these face recognition algorithms become properly trained and are deployed in a manner that does not result in false positives in the context of policing and judiciary, other solutions are needed. Smart makeup will improve the usage of this technology that has traditionally had a higher false positive identification rate for people of color and women.

BACKGROUND

Machine-learning based face recognition systems rely upon trainedartificial intelligence to match a scanned face with an existingdatabase of faces. Such systems are trained using a large set of faceimages. The training itself may include teaching a machine-learningbased system to match a feature set with a facial image allowing forreliable face recognition. Depending on the size and the rigor of suchtraining, the face recognition system can match a facial image to anentry in a database of images. Many such face recognition systemsextract features of a face based on observable characteristics of thesurface of a face.

Despite advances in the underlying technology associated with suchsystems, error rates, including false positives, remain an issue.

SUMMARY

In one example, the present disclosure relates to a including using afirst image sensor, acquiring image data for a first facial image of aperson. The method may further include processing the image data toselect a cosmetic application profile for the first facial image, wherethe cosmetic application profile is selected to modify the image data toalter a value of at least one image classification feature used by aface recognition algorithm in order to increase a false positiveidentification rate of the face recognition algorithm that is configuredto match an input facial image with at least one of N stored facialimages, where N is greater than 1,000.

The method may further include by processing the selected cosmeticapplication profile, generating a set of values corresponding to amagnetic field pattern for use with a magnetic field applicator andtransmitting the set of values to the magnetic field applicator. Themethod may further include using the magnetic field applicator, based onthe set of values, applying magnetic fields to nanoparticles embedded ina cosmetic composition applied to the face of the person, such that asecond facial image of the face, when acquired by a second image sensorsubsequent to the application of the magnetic fields is different fromthe first facial image acquired by the first image sensor.

In another example, the present disclosure relates to a including usinga first image sensor, acquiring image data for a first facial image of aperson, where the image data is acquired by positioning the first imagesensor at least 20 feet away from the face of the person. The method mayfurther include processing the image data to select a cosmeticapplication profile for the first facial image, where the cosmeticapplication profile is selected to modify the image data to alter avalue of at least one image classification feature used by a facerecognition algorithm in order to increase a false positiveidentification rate of the face recognition algorithm that is configuredto match an input facial image with at least one of N stored facialimages, where N is greater than 1,000.

The method may further include by processing the selected cosmeticapplication profile, generating a set of values corresponding to amagnetic field pattern for use with a magnetic field applicator andtransmitting the set of values to the magnetic field applicator. Themethod may further include using the magnetic field applicator, based onthe set of values, applying magnetic fields to nanoparticles embedded ina cosmetic applied to the face of the person to modify an appearance ofthe cosmetic, such that a second facial image of the face, when acquiredby a second image sensor subsequent to the application of the magneticfields is different from the first facial image acquired by the firstimage sensor.

In yet another example, the present disclosure relates to a includingevaluating a face recognition algorithm that is configured to match aninput facial image with at least one of N stored facial images, where Nis greater than 10,000, to determine a relationship between imageclassification features and a false positive identification rate of theface recognition algorithm. The method may further include using a firstimage sensor, acquiring image data for a first facial image of theperson, where the image data is acquired by positioning the image sensorat least 40 feet away from the face of the person. The method mayfurther include processing the image data to select a cosmeticapplication profile for the first facial image, where the cosmeticapplication profile is selected to modify the image data to alter avalue of at least one of the image classification features in order toincrease the false positive identification rate of the face recognitionalgorithm.

The method may further include generating a set of values correspondingto a magnetic field pattern for use with a magnetic field applicator byprocessing the selected cosmetic application profile and transmittingthe set of values to the magnetic field applicator. The method mayfurther include based on the set of values applying magnetic fields,using the magnetic field applicator, to nanoparticles embedded in acosmetic applied to the face of the person to modify an appearance ofthe cosmetic such that a second facial image of the face when acquiredby a second image sensor, different from the first image sensor,subsequent to the application of the magnetic fields is altered toincrease the false positive identification rate of the face recognitionalgorithm when detecting a 1:M match between the second facial image andM stored facial images, where M is greater than 10,000.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and is notlimited by the accompanying figures, in which like references indicatesimilar elements. Elements in the figures are illustrated for simplicityand clarity and have not necessarily been drawn to scale.

FIG. 1 is a diagram of a system environment for applying smart makeup inaccordance with one example;

FIG. 2 is a diagram showing components of a device in accordance withone example;

FIG. 3 is a diagram showing a band and an application of a magneticfield to a cosmetic composition applied to the face in accordance withone example;

FIG. 4 is a diagram showing a wand and an application of a magneticfield to a cosmetic composition applied to the eye in accordance withone example;

FIG. 5 is a diagram showing example components of a magnetic fieldapplicator for applying a magnetic field to a cosmetic composition;

FIG. 6 is a diagram showing components of a magnetic field generator ofFIG. 5 in accordance with one example;

FIG. 7 shows an example system for evaluating the false positiveidentification rate of a face recognition algorithm in relation tochanges to one or more values associated with the image classificationfeatures;

FIG. 8 is a block diagram of a computing system for implementing thesystem of FIG. 7 in accordance with one example;

FIG. 9 is a flow chart of a method associated with the variousembodiments; and

FIG. 10 is a flow chart of a method associated with the variousembodiments.

DETAILED DESCRIPTION

Security cameras can recognize faces using face recognition technology.Face recognition technology may identify specific features of a person'sface and compare them to a database of known faces. Such systems firstuse a camera to capture an image of a person's face and isolate the facefrom the background or other undesirable captured aspects of the image.Next, face recognition technology may extract specific features from theface, such as the distance between the eyes, the size or shape of theeyes, the shape and the size of the forehead, the shape of the nose, andthe size of the mouth, etc.

Next, the face recognition technology may analyze these features using atrained artificial intelligence neural network to determine whether asufficient number of the features show a match with an image in adatabase of known faces (known as 1:N matching). This process mayinclude the use of convolution neural networks (CNNs), recursive neuralnetworks (RNNs), or other types of neural networks that have beentrained to extract features from an image and then classify the image asmatching (or not) one or more of the known images of the faces. Thetraining process itself relies upon techniques, such as stochasticgradient descent (SGD) to determine appropriate weights and updatedweights for use in training the neural network. Once trained, the neuralnetwork model is used to process the extracted facial features andcompare them with the features of stored face images to determine amatch.

Increasingly, face recognition technology is being used in applicationsbeyond biometric identification for authentication/login purposes. As anexample, face recognition technology has been deployed as part ofsurveillance cameras, which may capture facial images that can be usedas evidence of criminal conduct in a court of law. While admissibilityof such evidence is subject to the rules of evidence used for any otherpiece of evidence, reliance on such face recognition technology poseschallenges. These challenges include privacy violations and improper useof such technology in policing and identifying suspects by police orother law enforcement agencies.

Moreover, many face recognition systems have demonstrated higher errorrates (e.g., false positives) with respect to facial images of people,whose faces may be different from the training dataset of images used totrain such systems. As an example, in the United Kingdom, surveillancecameras that scan an individual's face and classify them as eithercriminal or innocent have begun integrating into the society. However, astudy found that the use of this technology in the UK led to a 98% rateof failure among African Americans. According to this study, the facerecognition technology falsely classified African Americans ascriminal/suspicious at a rate of 98%, and sometimes falsely classifiedwhite criminals as innocent at a higher rate than criminal.

Not only this, but an American Civil Liberties Union (ACLU) briefsummarizing the Lynch v. State case stated that “Willie Lynch wassentenced to eight years in prison after the police implicated him usingan unproven, error-prone face recognition algorithm . . . the staterelied on the algorithm as the cornerstone of its investigation.” Untilthese face recognition algorithms become properly trained and aredeployed in a manner that does not result in false positives in thecontext of policing and judiciary, other solutions are needed.

Makeup has become an essential part of human existence. Smart makeupconfigured using the application of intelligent force vectors can impactthe accuracy of facial recognition systems. Visually observablecharacteristics of a surface area having at least some particles (e.g.,nanoparticles) are a function of several factors, including anorientation, an arrangement, or a density of the nano-particles.Configuring a visual characteristic of the surface area may produceinteresting results with respect to applications of certain compositionsto certain surfaces. As an example, compositions used for cosmeticpurposes may be configured based on the application of intelligent forcevectors. Such use of makeup is referred to herein as smart makeup.

Smart makeup works against these biases. Smart makeup uses nanoparticlesto alter feature analysis performed by face recognition algorithms.Smart makeup can be used to plant seeds of doubt in the context ofmatching an image with a database of images, which works by extractingfeatures. Its existence lowers trust in biometrics being used in policedepartments and courts. In the ACLU brief, the state was relying on theevidence based on face recognition technology as a huge aspect of theircase. Smart makeup will move law and investigation away from the usageof this technology (until they are improved) that often falselycategorizes people of color at a higher rate than the population as awhole in countries where the training datasets are skewed in the otherdirection. In the countries with the population having a different mix,the use of this technology may help people other than the people ofcolor. In sum, until facial recognition technology reaches a level ofaccuracy that is appropriate for use as part of law enforcement andjudiciary, its use may be further constrained or otherwise impacted bythe use of smart makeup. At the same time, smart makeup may be used tobetter train face recognition algorithms, such that they are properlytrained, and thus are less likely to produce false positives or falsenegatives.

FIG. 1 is a schematic diagram of an example system environment 100 forvarious methods and systems associated with the present disclosure,including applying smart makeup. A user of a mobile device 120 may applya cosmetic composition to a portion 140 of their face or any other partof their body. In one embodiment, the user may wear a band 150 on theirhead or another portion of their body depending upon the application ofthe cosmetic composition area. Band 150 may communicate via localnetworks 160 with mobile device 120 and other networks, such as wirelessnetworks 170 and sensor networks 180. Local networks 160 and wirelessnetworks 170 may include cellular networks, Wi-Fi networks, PersonalArea Networks, such as Bluetooth, or other types of wireless networks.Wireless networks 170 may include not only communication apparatuses,such as cell sites, but also cloud computing infrastructure. The cloudcomputing infrastructure may be used to provide additional computing andstorage functionality to mobile device 120. Sensor networks 180 mayallow mobile device 120 and band 150 to engage in machine-to-machinecommunication with other devices and sensors. While FIG. 1 showsseparate sensor networks 180, the functionality related to thesenetworks may be included in wireless networks 170. In addition, althoughFIG. 1 shows a band 150 that the user is shown as wearing, similarfunctionality could be achieved via an attachment to mobile device 120.

FIG. 2 is a diagram showing example components of an example device 200for implementing device 120 of the example system environment of FIG. 1. In one embodiment, device 200 may include a processor 202, memory 204,camera 206, and user input devices 208, battery 210, sensors 212, touchscreen display 214, and network interfaces 216. Each of these componentsmay be connected to each other (as needed for the functionality ofdevice 200) via a bus system 220. Example mobile devices include asmartphone, such as an iPhone or any other similar device. Processor 202may execute instructions stored in memory 204. Camera 206 may captureboth still and moving images. User input devices 208 include hapticdevices, such as keyboards or buttons, and touch screens. Battery 210may be any portable battery, such as a chargeable lithium-ion battery.Sensors 212 may include a variety of sensors, such as accelerometers,gyroscopes, GPS, and proximity sensors. Touch screen display 214 may beany type of display, such as LCD, LED, or other types of display. As anexample, touch screen display 214 may be a capacitive touch screen. Thetouch screen (e.g., display 214) can detect touch events, such astapping on the screen or swiping on the screen. In response to suchevents, in combination with other modules, described later, touch imagedata may be generated and submitted to processor 202. Network interfaces216 may include communication interfaces, such as cellular radio,Bluetooth radio, UWB radio, or other types of wireless or wiredcommunication interfaces. Although FIG. 2 shows a specific number ofcomponents arranged in a certain manner, device 200 may includeadditional or fewer components arranged differently.

FIG. 3 is a diagram showing a band 300 and application of magnetic fieldto a cosmetic composition applied to the face. Band 300 may correspondto band 150 of FIG. 1 . In one embodiment, band 300 may include ahousing 302. Housing 302 may include magnets 304 and a magnetic fieldgeneration unit. The magnetic field generation unit may control magnets304 to generate a magnetic field corresponding to a magnetic fieldpattern. Magnets 304 may be soft iron magnetic coils that generate amagnetic field when a current is driven through the coil. The strengthof the magnetic field will depend on the amount of current passedthrough the coil. Each magnet 304 may be controlled individually, or ingroups, to generate a magnetic field based on a magnetic field pattern.Band 300 may thus be used to apply a magnetic field based on a magneticfield pattern to cosmetic composition applied to a body portion, such asa face of a person. Thus, as an example, a person may have a cosmeticcomposition applied to a portion 306 of their face. That cosmeticcomposition may have a certain appearance 308. Upon application of acontrolled magnetic field, for example, using band 300, the appearance308 of the cosmetic composition may change to a different appearance312. The change in appearance may relate to a change in attributesrelated to the cosmetic composition. Example attributes include color,brightness, reflectivity, glint, iridescence, or tone. As used hereinthe term cosmetic composition is not limited to makeup, but alsoincludes other types of compositions, such as face paint.

FIG. 4 is a diagram showing a wand 400 and the application of a magneticfield to a cosmetic composition applied near the eye, such as eyelids.In one embodiment, wand 400 may include a housing 402. Housing 402 mayinclude magnets 404 and a magnetic field generation unit. The magneticfield generation unit may control magnets 404 to generate a magneticfield corresponding to a magnetic field pattern. Magnets 404 may be softiron magnetic coils that generate a magnetic field when a current isdriven through the coil. The strength of the magnetic field will dependon the amount of current passed through the coil. Each magnet 404 may becontrolled individually, or in groups, to generate a magnetic fieldbased on a magnetic field pattern. Wand 400 may thus be used to apply amagnetic field based on a magnetic field pattern to cosmetic compositionapplied to a body portion, such as a face of a person. Thus, as anexample, a person may have a cosmetic composition applied to a portion406 of their face. In this embodiment, portion 406 may be the areabetween the eyelashes and the eyebrows of the person, including, forexample, the eyelid. That cosmetic composition may have a certainappearance 408. Upon application of a controlled magnetic field, forexample, using wand 400, the appearance 408 of the cosmetic compositionmay change to a different appearance 412. The change in appearance mayrelate to a change in attributes related to the cosmetic composition.

Example attributes include color, brightness, reflectivity, glint,iridescence, or tone. In one embodiment, the cosmetic composition maynot appear to be as colorful and iridescent for a day look. Once theperson applies a controlled magnetic field to portion 406, it mayreorient certain particles in the cosmetic composition to make thecosmetic composition more colorful and iridescent. This way a person maygo from a day look to a night look in an instant. As another example,application of the controlled magnetic field to portion 406 may reorientcertain particles in the cosmetic composition to make it look glitteryfor the night look. Although FIGS. 3 and 4 describe a band and a wand,other types of applicators may also be used for applying the controlledmagnetic field. As an example, the magnetic field applicator could bedisc-shaped, cube-shaped, oval-shaped, or other types of shapes andsizes.

In one embodiment, cosmetic composition may include magnetic particlesthat are sensitive to a magnetic field. Cosmetic composition may includenon-magnetic particles as well, such as colorants etc. Magneticparticles may be particles that may include nickel, cobalt, iron andoxides or alloys of these metals. As an example, magnetic particles mayinclude iron oxide, Fe₃O₄. In one embodiment, magnetic particles may beelongate in shape, such that they may be aligned in a direction of themagnetic field. They may be aspherical or spherical with non-uniformshape. This way when their orientation is changed in response to theapplication of a magnetic field, it may result in a change in theappearance of cosmetic composition applied to a body portion, such asthe face of a person. In addition to magnetic particles, magneticfibers, or composite magnetic particles may be used as part of cosmeticcompositions. Additional details regarding cosmetic compositionsincluding magnetic particles are described in U.S. Patent PublicationNo. 2013/0160785, which is incorporated by reference herein in itsentirety. In particular, paragraphs 76 to 145 of this publicationdescribe magnetic particles, magnetic fibers, and composite magneticparticles, each of which could be used as part of cosmetic compositions.

In addition to elements that are susceptible to a magnetic field,cosmetic composition may further include diffractive pigments, which arecapable of producing a variation in color based on the angle ofobservation when hit by visible light. Cosmetic composition may furtherinclude reflective particles, which can reflect light and depending upontheir orientation (affected by magnetic field), they might reflect lightat different angles. Cosmetic composition may further include “nacres,”which may optionally be iridescent, as produced in the shells of certainmollusks. Nacres may have a yellow, pink, red, bronze, gold, or copperyglint, which could produce a metallic look. Additional details regardingcosmetic compositions including diffractive pigments and reflectiveparticles are described in U.S. Patent Publication No. 2013/0160785,which is incorporated by reference herein in its entirety. Inparticular, paragraphs 157 to 201 of the '785 publication describediffractive pigments, reflective particles, and nacres, each of whichcould be used as part of cosmetic compositions.

Additionally, or alternatively to the particles described above,cosmetic composition may further include fillers, which may helpmaintain the texture of the cosmetic composition. Additional detailsregarding cosmetic compositions including fillers are described in U.S.Patent Publication No. 2013/0160785, which is incorporated by referenceherein in its entirety. In particular, paragraphs 202 to 205 of the '785publication describe fillers.

Additionally, or alternatively to the particles described above,cosmetic composition may further include composite pigments, which maybe composed of particles including a magnetic core and a coating of anorganic coloring substance. Additional details regarding cosmeticcompositions including composite pigments are described in U.S. PatentPublication No. 2013/0160785, which is incorporated by reference hereinin its entirety. In particular, paragraphs 224 to 316 of the '785publication describe composite pigments.

Additionally, or alternatively to the particles described above,cosmetic composition may further include photochromic agents, whose tintchanges when they are lit by ultraviolet light and the tint returns toits initial color when no longer lit. Additional details regardingcosmetic compositions including photochromic agents are described inU.S. Patent Publication No. 2013/0160785, which is incorporated byreference herein in its entirety. In particular, paragraphs 317 to 316of the '785 publication describe composite pigments.

In another embodiment, cosmetic composition may include magneticallyresponsive photonic nanochains. Such magnetically responsive photonicnanochains may include iron oxide, Fe₃O₄ particles clustered with a thinlayer of silica. The nanochains may diffract light differently dependingupon the application of a magnetic field to such nanochains. Additionaldetails regarding magnetically responsive photonic nanochains aredescribed in U.S. Patent Publication No. 2014/0004275, which isincorporated by reference herein in its entirety. In particular,paragraphs 12 to 23 of the '275 publication describe magneticallyresponsive photonic nanochains and a process for forming them.

In one embodiment, cosmetic composition may include photochromic agentsthat are sensitive to radiation of certain wavelengths. Irradiation ofsuch photochromic agents, which can be included in the cosmeticcomposition, may result in the look of the makeup. Additional detailsregarding such light-sensitive makeup are described in U.S. PatentPublication No. 2010/0243514, which is incorporated by reference hereinin its entirety. In particular, paragraphs 63 to 109 of the '514publication provide examples of photochromic agents that may be includedin the cosmetic compositions.

In one embodiment, cosmetic composition may be such that afterapplication of the magnetic field, the skin color lightens. In thismanner, the cosmetic composition may be used to lighten skin color, suchas the color of the face, arms, or other body parts, based on theapplication of the magnetic field. Alternatively, in one embodiment,cosmetic composition may be such that after application of the magneticfield, the skin color looks tanned. In this manner, the cosmeticcomposition may be used to tan skin, such as tanning of the face, arms,or other body parts. Such changes may be accomplished by changing thelevel of melanin in the skin. Moreover, particles such as tin-oxide thatare used in sunscreens may also be used to lighten or otherwise changethe appearance of the facial skin.

FIG. 5 is a diagram showing example components of a magnetic fieldapplicator 500 for applying a magnetic field to a cosmetic composition.In one embodiment, magnetic field applicator 500 may include acontroller 502, memory 504, magnetic field pattern generator 506,battery 508, sensors 510, and network interfaces 512. Each of thesecomponents may be connected to each other (as needed for thefunctionality of magnetic field applicator 500) via a bus system 520.Controller 502 may execute instructions stored in memory 504. Battery508 may be any portable battery, such as a chargeable lithium-ionbattery. Sensors 510 may include a variety of sensors, such asaccelerometers, gyroscopes, GPS, and proximity sensors. Networkinterfaces 512 may include communication interfaces, such as cellularradio, Bluetooth radio, UWB radio, or other types of wireless or wiredcommunication interfaces. Controller 502 may control the operation ofmagnetic field applicator 500 by processing data stored in memory 504and managing interaction with other devices or networks via networkinterfaces 512. In addition, controller 502 may process data and controlthe behavior of sensors 512. Although FIG. 5 shows a specific number ofcomponents arranged in a certain manner, magnetic field applicator 500may include additional or fewer components arranged differently.

In one embodiment, sensors 510 may include an ambient light sensor. Theambient light sensor may sense the intensity of the light in the roomwhere a person is applying the cosmetic composition. The measuredintensity of light may be communicated to controller 502 via bus 520.Controller 502 may process the data related to the light intensity anduse that as a factor in controlling the operation of magnetic fieldapplicator 500. As an example, controller 502 may alter inputs to (orcontrol otherwise) magnetic field generator 506 in a way that in a roomwith less light the cosmetic composition is affected in a manner to bemore reflective. This may be accomplished by altering the degree oforientation of magnetic particles or other constituents of the cosmeticcomposition, including, for example, nanochains. In another embodiment,controller 502 may receive sensor data relating to the intensity oflight in a room or location that the person will be in subsequent toapplying the cosmetic composition. This sensor data may be communicatedvia device 200 of FIG. 2 or directly to controller 502. Controller 502may process the data related to the light intensity and use that as afactor in controlling the operation of magnetic field applicator 500. Asan example, controller 502 may alter inputs to, or control otherwise,magnetic field generator 506 in a way that in a room with less light thecosmetic composition is affected in a manner to be more reflective. Thismay be accomplished by altering the degree of orientation of magneticparticles or other constituents of the cosmetic composition, including,for example, nanochains. Other attributes, such as color, brightness,reflectivity, glint, iridescence, or tone may also be affected usingsimilar methodology.

FIG. 6 is a diagram showing example components of an example magneticfield pattern generator 506 of FIG. 5 . In one embodiment, magneticfield pattern generator 506 may include a pixel values to analog voltagegenerator (PAVG) 610, an analog voltage to current generator (ACG) 620,and a current conditioner 630. PAVG 610 may be implemented using adigital to analog converter, such that the pixels (black or white) maybe converted into a corresponding analog voltage. In one embodiment,PAVG 610 may also include gamma correction to correct for the non-linearway human eye processes light. Additional details regarding a pixelvalues to analog voltage generator, including gamma correction, aredescribed in U.S. Pat. No. 7,724,171, which is incorporated by referenceherein in its entirety. In particular, FIGS. 2 and 3 and the relateddescription in the '171 patent describe a pixel values to analog voltagegenerator, including gamma correction. Analog voltage to currentgenerator (ACG) 620 may convert the analog voltages into currents. Inone embodiment, ACG 620 may be implemented using the PrecisionVoltage-to-Current Converter/Transmitter (XTR111) sold by TexasInstruments. Current conditioner 630 may be used to amplify the currentsgenerated by ACG 620. The current conditioner 630 may also be used to:(1) increase the resolution of the currents generated by ACG 620, and(2) improve the signal to noise ratio. The currents conditioned bycurrent conditioner 630 may be used to energize the magnetic coils.Although FIG. 6 shows a specific number of components arranged in acertain manner, magnetic field pattern generator 506 may includeadditional or fewer components arranged differently.

Referring back to FIG. 5 , controller 502 is configured to executeinstructions stored in memory 504 and generate a set of valuescorresponding to a magnetic field pattern for use with field patterngenerator 506 of FIG. 5 . The set of values may relate to the intensityof the magnetic field and the orientation of the magnetic field. In thisexample, controller 502 generates the set of values, in part, byprocessing the selected cosmetic application profile and transmittingthe set of values to the field pattern generator 506 associated with themagnetic field applicator 500. Additionally, or alternatively, the setof values may be generated by trial and error processes to determine thetype of values that best capture a given cosmetic application profilethat can be used to modify the orientation or other aspects ofnanoparticles included in the cosmetic composition applied to the faceof the person. The set of values could be stored in a memory (e.g.,memory 504 of FIG. 5 ). In other words, the set of values could simplybe selected based on the cosmetic application profile stored in memory504 of FIG. 5 . The selection may be made based on a look-up table thatcorrelates each cosmetic application profile to a corresponding set ofvalues for field pattern generator 506 of FIG. 5 .

Various modules including instructions may be stored in a memory ofdevice 200 of FIG. 2 for processing image data to: (1) acquiring imagedata for a facial image of a person and processing the image data toselect a cosmetic application profile for the facial image. In oneembodiment, these modules may be stored in memory 204 of device 200 andmay contain software instructions that when executed by processor 202 ofdevice 200 may provide the functionality associated with these modules.

In addition, memory 204 of device 200 of FIG. 2 may store images of bodyportions with cosmetic compositions applied to the body portions.Furthermore, images may be stored in remote storage locations and couldbe accessed via local networks 160 or wireless networks 170 by mobiledevice 120. The cosmetic composition module (e.g., in memory 204 ofdevice 200 of FIG. 2 ) may include instructions that when executed byprocessor 202 of FIG. 2 may result in processing of image datacorresponding to such images to generate a cosmetic application profilefor a relevant portion of the body. Instructions related to the cosmeticapplication profile may be stored in memory 204 of device 200. Cosmeticapplication profile may be any digital representation of attributes,such as color, brightness, reflectivity, glint, iridescence, or tonethat are affected by the application of cosmetic compositions. In oneexample, it could be a spatial mapping of each of these attributes foreach pixel of the image data and its location on the body portion.Alternatively, it could be a spatial mapping of a subset of theseattributes for each pixel of the image data.

As explained earlier, face recognition technology may identify specificfeatures of a person's face and compare them to a database of knownfaces. Such systems first use a camera to capture an image of a person'sface and isolate the face from the background or other undesirablecaptured aspects of the image. Next, face recognition technology mayextract specific features from the face, such as the distance betweenthe eyes, the size or shape of the eyes, the shape and the size of theforehead, the shape of the nose, and the size of the mouth etc. Next,the face recognition technology may analyze these features using atrained artificial intelligence neural network to determine whether asufficient number of the features show a match with an image in adatabase of known faces (known as 1:N matching). This process mayinclude the use of convolution neural networks (CNNs), recursive neuralnetworks (RNNs), or other types of neural networks that have beentrained to extract features from an image and then classify the image asmatching (or not) one or more of the known images of the faces.

Face recognition algorithms are imperfect and can create falsepositives. False positive rates vary across races and gender. Studiesconducted by the National Institute of Science and Technology, a USGovernment agency, have shown that false positive rates not only varyfrom a face recognition algorithm to another face recognition algorithm,but also vary demographically across race and gender. As one example,some studies have found the highest false positive rates for NativeAmerican women and elevated false positive rates in African American andAsian populations relative to white populations. False positive rateshave been shown to be higher due to race rather than gender.

Face recognition algorithms are not built to identify particular people;instead, they include a face detector followed by a feature extractionalgorithm that converts one or more images of a person into a vector ofvalues that relate to the identity of the person. The extractortypically consists of a neural network that has been trained onID-labeled images available to the developer. Thus, face recognitionalgorithms act as generic extractors of identity-related informationfrom photos of people they have usually never seen before. Recognitionproceeds in the following manner. First, the face recognition algorithmscompare two feature vectors and emit a similarity score. This is anumeric value (specific to each face recognition algorithm) expressinghow similar the faces that are being compared are. Second, this numericvalue is then compared to a threshold value to decide whether twosamples represent the same person or not. Thus, recognition is mediatedby persistent identity information stored in a feature vector (or a“template”).

FIG. 7 shows an example system 700 for evaluating the false positiveidentification rate of a face recognition algorithm in relation tochanges to one or more values associated with the image classificationfeatures. Functional blocks associated with system 700 are shown toillustrate the performance of a 1:N match to produce a candidate list.System 700 includes a first feature extraction block 710, a database ofN facial images 720, a detection and localization block 730, a secondfeature extraction block 740, and a search algorithm block 750. Firstfeature extraction block 710 is used to extract features that comprisethe feature vector, which encodes the identity of a person. In thisexample, first feature extraction block 710 extracts the feature vectorsfor all N stored images in the database of N facial images 720.Detection and localization block 730 is used to process the image dataobtained from an image sensor associated with a camera that captured afacial image of a person. The second feature extraction block 730 isused to extract features that comprise the feature vector, which encodesthe identity of a person. In this example, the second feature extractionblock 730 extracts the feature vector for the facial image obtained bythe image sensor. The extracted feature vector is compared by searchalgorithm block 750 against the extracted feature vectors for the Nstored images. Search algorithm block 750 generates an output that mayinclude a candidate list for any potential matches between the facialimage obtained via the image sensor and any of the stored N facialimages.

Table 1 below shows the data structures/types that may be used as partof evaluating a relationship between the image classification featuresand a false positive identification rate of a face recognitionalgorithm.

Data Structure/ Type Explanation Feature A vector of real numbers thatencodes the identity Vector of a person Sample One or more images of aperson Similarity A measure of the degree of similarity of two facesScore in two samples, as determined by a face recognition algorithmTemplate Data produced by a face recognition algorithm that includes afeature vector Threshold Any real number against which the similarityscores are

The candidate list output by system 700 is evaluated to determine thefalse positive identification rate. In one example, any search resultsthat include a facial image that is not in the database of stored imagesare considered non-mated search results. In other words, when system 700outputs a positive match between a facial image of a person that hasnever been seen by system 700 but is incorrectly associated with afacial image in the stored image database, that search result is countedas a non-mated search result. Assume that system 700 is configured withan enrolled population of N identities (e.g., one each for the N storedimages in database of N facial images 720) and the search algorithm isconfigured to generate L candidate identities that are ranked by theirsimilarity score. The L candidate identities are a subset of theidentified images and include only those images that had a similarityscore above a preselected threshold T. In this case, the false positiveidentification rate can be determined using the following equation: FPIR(N, T)=(Number of non-mated searches with one or more candidates thathad a similarity score above the threshold value (T)) divided by (thetotal number of non-mated searches attempted). In this example, thethreshold value is a fixed threshold value, which is the same for eachdemographic and is not tailored for a specific demographic.

As explained earlier, the goal is to increase the FPIR of the facerecognition algorithm by modifying the image classification featuresused by the second feature extraction block 730 of FIG. 7 . The imageclassification features are any features that contribute to the featurevector used to determine the similarity score. Not all imageclassification features need to be modified. Only a subset of the imageclassification features that can be controlled using the processes andsteps described herein are modified. By repeatedly presenting non-matedfacial images with varying degrees of modifications (e.g., caused bysubjecting the particles embedded in cosmetic compositions to magneticfields), the FPIR for various modifications of the image classificationfeatures can be obtained. Once such modifications and their impact onthe FPIR have been evaluated, only those modifications that increase theFPIR are programmed for use with the devices described herein. Generallyadversarial neural-networks (GANs) may be configured to process themodifications and the image data corresponding to images associated withthe face, and then may be pitted against each other to increase theFPIR.

As an example, any of the previously described particles may be includedin different samples of the cosmetic compositions and facial images ofthe same person may be acquired. Subsequently, the cosmetic compositionscould be subjected to the magnetic fields described earlier. Havingaltered at least some aspect of the cosmetic composition beingevaluated, a second facial image of the same person may be obtained.Through hit and trial, or other methods, appropriate combinations of theparticles with cosmetic compositions may be determined. As explainedearlier, the suitability of the cosmetic compositions and the particlesembedded therein is evaluated to determine their impact on the FPIR.Only those modifications of the particles for certain cosmeticcompositions that increase the FPIR are programmed for use with thedevices described herein.

The process for evaluating the efficacy of the particles in combinationwith the cosmetic compositions in the context of increasing the FPIRneed not be performed using actual facial images of a person. Instead,simulated images incorporating the impact of the application of themagnetic fields to the cosmetic composition with certain embeddedparticles may be evaluated. Such simulated images may be created afterexperimenting with various combinations of particles embedded incosmetic compositions and an application of the magnetic fields to suchcosmetic compositions. The simulated images may also be created usingother techniques for studying the impact of cosmetic compositions on thesurface area associated with the face.

FIG. 8 is a block diagram of a computing system 800 for implementing thesystem 700 of FIG. 7 in accordance with one example. Computing system800 may be a distributed computing system including components housed indata centers, on customers' premises, or any other location. As anexample, computing system 800 is used to implement the various parts ofthe components, services, layers, processes, and datastores describedherein. Computing system 800 includes a processor(s) 802, I/Ocomponent(s) 804, a memory 806, presentation component(s) 808, sensor(s)810, database(s) 812, networking interfaces 814, and 1/O port(s) 816,which may be interconnected via bus 820. Processor(s) 802 may executeinstructions stored in memory 806 or any other instructions received viaa wired or a wireless connection. Processor(s) 802 may include CPUs,GPUs, Application-Specific Integrated Circuits (ASICs),Field-Programmable Gate Arrays (FPGAs), or other types of logicconfigured to execute instructions. 1/O component(s) 804 may includecomponents such as a keyboard, a mouse, a voice recognition processor,or touch screens. Memory 806 may be any combination of non-volatilestorage or volatile storage (e.g., flash memory, DRAM, SRAM, or othertypes of memories). Presentation component(s) 808 may includedisplay(s), holographic device(s), or other presentation device(s).Display(s) may be any type of display, such as LCD, LED, or other typesof display. Sensor(s) 810 may include telemetry or other types ofsensors configured to detect, and/or receive, information (e.g.,conditions associated with the various devices in a data center).Sensor(s) 810 may include sensors configured to sense conditionsassociated with CPUs, memory or other storage components, FPGAs,motherboards, baseboard management controllers, or the like.

Still referring to FIG. 8 , database(s) 812 may be used to store any ofthe data or files (e.g., metadata store or other datasets) needed forthe performance of the various methods and systems described herein.Database(s) 812 may be implemented as a collection of distributeddatabases or as a single database. Network interface(s) 814 may includecommunication interfaces, such as Ethernet, cellular radio, Bluetoothradio, UWB radio, or other types of wireless or wired communicationinterfaces. 1/O port(s) 816 may include Ethernet ports, Fiber-opticports, wireless ports, or other communication ports.

Instructions for enabling various systems, components, devices, methods,services, layers, and processes may be stored in memory 806 or anothermemory. These instructions when executed by processor(s) 802, or otherprocessors, may provide the functionality associated with the varioussystems, components, devices, services, layers, processes, and methodsdescribed in this disclosure. The instructions could be encoded ashardware corresponding to a processor or a field programmable gatearray. Other types of hardware such as ASICs and GPUs may also be used.The functionality associated with the systems, services, devices,components, methods, processes, and layers described herein may beimplemented using any appropriate combination of hardware, software, orfirmware. Although FIG. 8 shows computing system 800 as including acertain number of components arranged and coupled in a certain way, itmay include fewer or additional components arranged and coupleddifferently. In addition, the functionality associated with computingsystem 800 may be distributed or combined, as needed.

FIG. 9 is an example flow chart 900 of a method associated with thevarious embodiments described herein. The instructions associated withthis method may be stored in the memory of the various devices andsystems described herein. Step 910 includes using a first image sensor,acquiring image data for a first facial image of a person. In oneexample, instructions (along with the camera) that are stored in amemory (e.g., memory 204) of device 200 of FIG. 2 are used for acquiringimage data for a facial image of a person.

Step 920 includes processing the image data to select a cosmeticapplication profile for the first facial image, where the cosmeticapplication profile is selected to modify the image data to alter avalue of at least one image classification feature used by a facerecognition algorithm in order to increase a false positiveidentification rate of the face recognition algorithm that is configuredto match an input facial image with at least one of N stored facialimages, where N is greater than 1,000. Various instructions stored in amemory of device 200 of FIG. 2 may be used for processing the image datato select a cosmetic application profile for the facial image. Asexplained earlier, the goal is to increase the FPIR of the facerecognition algorithm by modifying the image classification featuresused by the second feature extraction block 730 of FIG. 7 . The imageclassification features are any features that contribute to the featurevector used to determine the similarity score. Not all imageclassification features need to be modified. Only a subset of the imageclassification features that can be controlled using the processes andsteps described herein are modified. By repeatedly presenting non-matedfacial images with varying degrees of modifications (e.g., caused bysubjecting the particles embedded in cosmetic compositions to magneticfields), the FPIR for various modifications of the image classificationfeatures can be obtained.

As explained earlier, as an example, the candidate list output by system700 of FIG. 7 is evaluated to determine the false positiveidentification rate. In one example, any search results that include afacial image that is not in the database of stored images are considerednon-mated search results. In other words, when system 700 of FIG. 7outputs a positive match between a facial image of a person that hasnever been seen by system 700 but is incorrectly associated with afacial image in the stored image database, that search result is countedas a non-mated search result. Assume that system 700 of FIG. 7 isconfigured with an enrolled population of N identities (e.g., one eachfor the N stored images in database of N facial images 720 of FIG. 7 )and the search algorithm is configured to generate L candidateidentities that are ranked by their similarity score. The L candidateidentities are a subset of the identified images and include only thoseimages that had a similarity score above a preselected threshold T. Inthis case, the false positive identification rate can be determinedusing the following equation: FPIR (N, T)=(Number of non-mated searcheswith one or more candidates that had a similarity score above thethreshold value (T)) divided by (the total number of non-mated searchesattempted). In this example, the threshold value is a fixed thresholdvalue is the same for each demographic and is not tailored for aspecific demographic.

Once such modifications of image classification features and theirimpact on the FPIR have been evaluated, only those modifications thatincrease the FPIR are programmed for use with the devices describedherein. The cosmetic application profile selection will be based on thisanalysis to ensure that the selected cosmetic application profileresults in the increase in the FPIR of the one or more commonly usedface recognition algorithms.

Step 930 includes by processing the selected cosmetic applicationprofile, generating a set of values corresponding to a magnetic fieldpattern for use with a magnetic field applicator and transmitting theset of values to the magnetic field applicator. In one example,instructions (along with the camera) that are stored in a memory (e.g.,memory 204) of device 200 of FIG. 2 are used for generating the set ofvalues and transmitting those to the magnetic field applicator (e.g.,magnetic field applicator 500 of FIG. 5 ). Additional details associatedwith generating and transmitting the set of values to the magnetic fieldapplicator are described earlier with respect to FIGS. 5 and 6 .

Step 940 includes using the magnetic field applicator, based on the setof values, applying magnetic fields to nanoparticles embedded in acosmetic composition applied to the face of the person, such that asecond facial image of the face, when acquired by a second image sensorsubsequent to the application of the magnetic fields is different fromthe first facial image acquired by the first image sensor. As anexample, any of the previously described particles may be included indifferent samples of the cosmetic compositions and facial images of thesame person may be acquired. Nanoparticles may include metallicmolecules and melanin molecules attached to the metallic molecules.Cosmetic compositions may include diffractive pigments capable ofproducing a variation in color based on an angle of observation when hitby visible light. Cosmetic compositions may also include reflectiveparticles. Cosmetic compositions may also include composite pigmentsincluding a magnetic core and a coating of an organic coloringsubstance.

Subsequently, the cosmetic compositions could be subjected to themagnetic fields using the magnetic field applicator 500 describedearlier with respect to FIG. 5 . Having altered at least some aspect ofthe cosmetic composition being evaluated, a second facial image of thesame person may be obtained. Through hit and trial, or other methods,appropriate combinations of the particles with cosmetic compositions maybe determined. As explained earlier, the suitability of the cosmeticcompositions and the particles embedded therein is evaluated todetermine their impact on the FPIR. Only those modifications of theparticles for certain cosmetic compositions that increase the FPIR areprogrammed for use with the devices described herein.

FIG. 10 is another example flow chart 1000 of a method associated withthe various embodiments described herein. The instructions associatedwith this method may be stored in the memory of the various devices andsystems described herein. These instructions when executed may cause theassociated processor to perform the steps described as part of flowchart 1000. Step 1010 includes evaluating a face recognition algorithmthat is configured to match an input facial image with at least one of Nstored facial images, where N is greater than 10,000, to determine arelationship between image classification features and a false positiveidentification rate of the face recognition algorithm. This step mayinclude modifying the images being processed by the face recognitionalgorithm to evaluate which image classification features have an impactof the false positive identification rate of the face recognitionalgorithm. As explained earlier, the image classification features areany features that contribute to the feature vector used to determine thesimilarity score. Not all image classification features need to bemodified. Only a subset of the image classification features that can becontrolled using the processes and steps described herein are modified.By repeatedly presenting non-mated facial images with varying degrees ofmodifications (e.g., caused by subjecting the particles embedded incosmetic compositions to magnetic fields), the FPIR for variousmodifications of the image classification features can be obtained.

Step 1020 includes using a first image sensor, acquiring image data fora first facial image of the person, where the image data is acquired bypositioning the image sensor at least 40 feet away from the face of theperson. In one example, instructions (along with the camera) that arestored in a memory (e.g., memory 204) of device 200 of FIG. 2 are usedfor acquiring image data for a facial image of a person. Although thisstep refers to positioning the image sensor at least 40 feet away fromthe face of the person, depending upon the time of the day or otherconstraints, the image sensor may be positioned closer to the face orthe person or farther away.

Step 1030 includes processing the image data to select a cosmeticapplication profile for the first facial image, where the cosmeticapplication profile is selected to modify the image data to alter avalue of at least one of the image classification features in order toincrease the false positive identification rate of the face recognitionalgorithm. Various instructions stored in a memory of device 200 of FIG.2 may be used for processing the image data to select a cosmeticapplication profile for the facial image. As explained earlier, the goalis to increase the FPIR of the face recognition algorithm by modifyingthe image classification features used by the second feature extractionblock 730 of FIG. 7 . The image classification features are any featuresthat contribute to the feature vector used to determine the similarityscore. Not all image classification features need to be modified. Only asubset of the image classification features that can be controlled usingthe processes and steps described herein are modified. By repeatedlypresenting non-mated facial images with varying degrees of modifications(e.g., caused by subjecting the particles embedded in cosmeticcompositions to magnetic fields), the FPIR for various modifications ofthe image classification features can be obtained.

As explained earlier, as an example, the candidate list output by system700 of FIG. 7 is evaluated to determine the false positiveidentification rate. In one example, any search results that include afacial image that is not in the database of stored images are considerednon-mated search results. In other words, when system 700 of FIG. 7outputs a positive match between a facial image of a person that hasnever been seen by system 700 but is incorrectly associated with afacial image in the stored image database, that search result is countedas a non-mated search result. Assume that system 700 of FIG. 7 isconfigured with an enrolled population of N identities (e.g., one eachfor the N stored images in database of N facial images 720 of FIG. 7 )and the search algorithm is configured to generate L candidateidentities that are ranked by their similarity score. The L candidateidentities are a subset of the identified images and include only thoseimages that had a similarity score above a preselected threshold T. Inthis case, the false positive identification rate can be determinedusing the following equation: FPIR (N, T)=(Number of non-mated searcheswith one or more candidates that had a similarity score above thethreshold value (T)) divided by (the total number of non-mated searchesattempted). In this example, the threshold value is a fixed thresholdvalue is the same for each demographic and is not tailored for aspecific demographic.

Once such modifications of image classification features and theirimpact on the FPIR have been evaluated, only those modifications thatincrease the FPIR are programmed for use with the devices describedherein. The cosmetic application profile selection will be based on thisanalysis to ensure that the selected cosmetic application profileresults in the increase in the FPIR of the one or more commonly usedface recognition algorithms.

Step 1040 includes generating a set of values corresponding to amagnetic field pattern for use with a magnetic field applicator byprocessing the selected cosmetic application profile and transmittingthe set of values to the magnetic field applicator. In one example,instructions (along with the camera) that are stored in a memory (e.g.,memory 204) of device 200 of FIG. 2 are used for generating the set ofvalues and transmitting those to the magnetic field applicator (e.g.,magnetic field applicator 500 of FIG. 5 ). Additional details associatedwith generating and transmitting the set of values to the magnetic fieldapplicator are described earlier with respect to FIGS. 5 and 6 .

Step 1050 includes based on the set of values applying magnetic fields,using the magnetic field applicator, to nanoparticles embedded in acosmetic applied to the face of the person to modify an appearance ofthe cosmetic such that a second facial image of the face when acquiredby a second image sensor, different from the first image sensor,subsequent to the application of the magnetic fields is altered toincrease the false positive identification rate of the face recognitionalgorithm when detecting a 1:M match between the second facial image andM stored facial images, where M is greater than 10,000. Nanoparticlesmay include metallic molecules and melanin molecules attached to themetallic molecules. Cosmetic compositions may include diffractivepigments capable of producing a variation in color based on an angle ofobservation when hit by visible light. Cosmetic compositions may alsoinclude reflective particles. Cosmetic compositions may also includecomposite pigments including a magnetic core and a coating of an organiccoloring substance.

Subsequently, the cosmetic compositions could be subjected to themagnetic fields using the magnetic field applicator 500 describedearlier with respect to FIG. 5 . Having altered at least some aspect ofthe cosmetic composition being evaluated, a second facial image of thesame person may be obtained. Through hit and trial, or other methods,appropriate combinations of the particles with cosmetic compositions maybe determined. As explained earlier, the suitability of the cosmeticcompositions and the particles embedded therein is evaluated todetermine their impact on the FPIR. Only those modifications of theparticles for certain cosmetic compositions that increase the FPIR areprogrammed for use with the devices described herein.

Moreover, the increase in the FPIR of a face recognition algorithm mayalso be used to improve the face recognition algorithm. As an example,the image classification features that appear to result in a higher FPIRfor certain population groups may be supplemented with additional imageclassification features, including features that are less impacted bythe application of the cosmetic compositions. Such features may includethe distance between certain features of the eyes. The distance betweenthe eyes and the bottom of the nose and other such features.

It is to be understood that the methods, modules, and componentsdepicted herein are merely exemplary. Alternatively, or in addition, thefunctionality described herein can be performed, at least in part, byone or more hardware logic components. For example, and withoutlimitation, illustrative types of hardware logic components that can beused include Field-Programmable Gate Arrays (FPGAs),Application-Specific Integrated Circuits (ASICs), Application-SpecificStandard Products (ASSPs), System-on-a-Chip systems (SOCs), ComplexProgrammable Logic Devices (CPLDs), etc. In an abstract, but stilldefinite sense, any arrangement of components to achieve the samefunctionality is effectively “associated” such that the desiredfunctionality is achieved. Hence, any two components herein combined toachieve a particular functionality can be seen as “associated with” eachother such that the desired functionality is achieved, irrespective ofarchitectures or inter-medial components. Likewise, any two componentsso associated can also be viewed as being “operably connected,” or“coupled,” to each other to achieve the desired functionality. Merelybecause a component, which may be an apparatus, a structure, a system,or any other implementation of a functionality, is described herein asbeing coupled to another component does not mean that the components arenecessarily separate components. As an example, a component A describedas being coupled to another component B may be a sub-component of thecomponent B, or the component B may be a sub-component of the componentA.

The functionality associated with some examples described in thisdisclosure can also include instructions stored in a non-transitorymedia. The term “non-transitory media” as used herein refers to anymedia storing data and/or instructions that cause a machine to operatein a specific manner. Exemplary non-transitory media includenon-volatile media and/or volatile media. Non-volatile media include,for example, a hard disk, a solid state drive, a magnetic disk or tape,an optical disk or tape, a flash memory, an EPROM, NVRAM, PRAM, or othersuch media, or networked versions of such media. Volatile media include,for example, dynamic memory such as DRAM, SRAM, a cache, or other suchmedia. Non-transitory media is distinct from, but can be used inconjunction with transmission media. Transmission media is used fortransferring data and/or instruction to or from a machine. Exemplarytransmission media, include coaxial cables, fiber-optic cables, copperwires, and wireless media, such as radio waves.

Furthermore, those skilled in the art will recognize that boundariesbetween the functionality of the above described operations are merelyillustrative. The functionality of multiple operations may be combinedinto a single operation, and/or the functionality of a single operationmay be distributed in additional operations. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Although the disclosure provides specific examples, variousmodifications and changes can be made without departing from the scopeof the disclosure as set forth in the claims below. Accordingly, thespecification and figures are to be regarded in an illustrative ratherthan a restrictive sense, and all such modifications are intended to beincluded within the scope of the present disclosure. Any benefits,advantages, or solutions to problems that are described herein withregard to a specific example are not intended to be construed as acritical, required, or essential feature or element of any or all theclaims.

Furthermore, the terms “a” or “an,” as used herein, are defined as oneor more than one. Also, the use of introductory phrases such as “atleast one” and “one or more” in the claims should not be construed toimply that the introduction of another claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an.” The sameholds true for the use of definite articles.

Unless stated otherwise, terms such as “first” and “second” are used toarbitrarily distinguish between the elements such terms describe. Thus,these terms are not necessarily intended to indicate temporal or otherprioritization of such elements.

What is claimed:
 1. A method comprising: using a first image sensor,acquiring image data for a first facial image of a person; processingthe image data to select a cosmetic application profile for the firstfacial image, wherein the cosmetic application profile is selected tomodify the image data to alter a value of at least one imageclassification feature used by a face recognition algorithm in order toincrease a false positive identification rate of the face recognitionalgorithm that is configured to match an input facial image with atleast one of N stored facial images, wherein N is greater than 1,000; byprocessing the selected cosmetic application profile, generating a setof values corresponding to a magnetic field pattern for use with amagnetic field applicator and transmitting the set of values to themagnetic field applicator; and using the magnetic field applicator,based on the set of values, applying magnetic fields to nanoparticlesembedded in a cosmetic composition applied to the face of the person,such that a second facial image of the face, when acquired by a secondimage sensor subsequent to the application of the magnetic fields isdifferent from the first facial image acquired by the first imagesensor.
 2. The method of claim 1, wherein the nanoparticles includemetallic molecules.
 3. The method of claim 2, wherein the nanoparticlesinclude melanin molecules attached to the metallic molecules.
 4. Themethod of claim 1, wherein the cosmetic includes diffractive pigmentscapable of producing a variation in color based on an angle ofobservation when hit by visible light.
 5. The method of claim 1, whereinthe cosmetic includes reflective particles.
 6. The method of claim 1,wherein the cosmetic includes composite pigments including a magneticcore and a coating of an organic coloring substance.
 7. The method ofclaim 1, further comprising using a GPS sensor, determining a locationof the person, and using the location as part of selecting the cosmeticapplication profile.
 8. A method comprising: using a first image sensor,acquiring image data for a first facial image of a person, wherein theimage data is acquired by positioning the first image sensor at least 20feet away from the face of the person; processing the image data toselect a cosmetic application profile for the first facial image,wherein the cosmetic application profile is selected to modify the imagedata to alter a value of at least one image classification feature usedby a face recognition algorithm in order to increase a false positiveidentification rate of the face recognition algorithm that is configuredto match an input facial image with at least one of N stored facialimages, wherein N is greater than 1,000; by processing the selectedcosmetic application profile, generating a set of values correspondingto a magnetic field pattern for use with a magnetic field applicator andtransmitting the set of values to the magnetic field applicator; andusing the magnetic field applicator, based on the set of values,applying magnetic fields to nanoparticles embedded in a cosmetic appliedto the face of the person to modify an appearance of the cosmetic, suchthat a second facial image of the face, when acquired by a second imagesensor subsequent to the application of the magnetic fields is differentfrom the first facial image acquired by the first image sensor.
 9. Themethod of claim 8, wherein the nanoparticles include metallic molecules.10. The method of claim 9, wherein the nanoparticles include melaninmolecules attached to metallic molecules.
 11. The method of claim 10,wherein the cosmetic includes diffractive pigments capable of producinga variation in color based on an angle of observation when hit byvisible light.
 12. The method of claim 11, wherein the cosmetic includesreflective particles.
 13. The method of claim 12, wherein the cosmeticincludes composite pigments including a magnetic core and a coating ofan organic coloring substance.
 14. The method of claim 13, furthercomprising using a GPS sensor, determining a location of the person, andusing the location as part of selecting the cosmetic applicationprofile.
 15. A method comprising: evaluating a face recognitionalgorithm that is configured to match an input facial image with atleast one of N stored facial images, wherein N is greater than 10,000,to determine a relationship between image classification features and afalse positive identification rate of the face recognition algorithm;using a first image sensor, acquiring image data for a first facialimage of the person, wherein the image data is acquired by positioningthe image sensor at least 40 feet away from the face of the person;processing the image data to select a cosmetic application profile forthe first facial image, wherein the cosmetic application profile isselected to modify the image data to alter a value of at least one ofthe image classification features in order to increase the falsepositive identification rate of the face recognition algorithm;generating a set of values corresponding to a magnetic field pattern foruse with a magnetic field applicator by processing the selected cosmeticapplication profile and transmitting the set of values to the magneticfield applicator; and based on the set of values applying magneticfields, using the magnetic field applicator, to nanoparticles embeddedin a cosmetic applied to the face of the person to modify an appearanceof the cosmetic such that a second facial image of the face whenacquired by a second image sensor, different from the first imagesensor, subsequent to the application of the magnetic fields is alteredto increase the false positive identification rate of the facerecognition algorithm when detecting a 1:M match between the secondfacial image and M stored facial images, wherein M is greater than10,000.
 16. The method of claim 15, wherein the nanoparticles includemetallic molecules, and wherein the nanoparticles include melaninmolecules attached to the metallic molecules.
 17. The method of claim16, wherein the cosmetic includes diffractive pigments capable ofproducing a variation in color based on an angle of observation when hitby visible light.
 18. The method of claim 16, wherein the cosmeticincludes reflective particles.
 19. The method of claim 18, wherein thecosmetic includes composite pigments including a magnetic core and acoating of an organic coloring substance.
 20. The method of claim 19,further comprising using a GPS sensor, determining a location of theperson, and using the location as part of selecting the cosmeticapplication profile.